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Describing Geographical Characteristics with Social Images

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10132)

Abstract

Images play important roles in providing comprehensive understanding of our physical world. When thinking of a tourist city, one can immediately imagine pictures of its famous attractions. With the boom of social images, we attempt to explore the possibility of describing geographical characteristics of different regions. We here propose a Geographical Latent Attribute Model (GLAM) to mine regional characteristics from social images, which is expected to provide a comprehensive view of the regions. The model assumes that a geographical region consists of different “attributes” (e.g., infrastructures, attractions, events and activities) and “attributes” are interpreted by different image “clusters”. Both “attributes” and image “clusters” are modeled as latent variables. The experimental analysis on a collection of 2.5M Flickr photos regarding Chinese provinces and cities has shown that the proposed model is promising in describing regional characteristics. Moreover, we demonstrate the usefulness of the proposed model for place recommendation.

Keywords

Geographic characteristics Recommender systems Latent variable models Region description 

Notes

Acknowledgments

The work is partially supported by the High Technology Research and Development Program of China 2015AA015801, NSFC 61521062, STCSM 12DZ2272600.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.SJTU-ParisTech Elite Institute of TechnologyShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Cooperative Medianet Innovation CenterShanghai Jiao Tong UniversityShanghaiChina

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